DatriseAI-first ETL

Google Cloud Storage to Qlik Integration

AI-first ETL from Google Cloud Storage into Qlik. Governed entities, incremental sync, typed landing tables.

How Datrise loads Google Cloud Storage into Qlik

Datrise syncs Google Cloud Storage's buckets, objects, prefixes, object metadata, generations, and storage classes into Qlik as tables loaded into Qlik's associative engine (often via QVD). Flexible or custom fields land in flattened columns for the data model, and timestamps such as created, updated, and status changes are typed as date/time fields.

Sync is incremental: Datrise uses incremental QVD loads merged on stable id, so re-runs update only what changed. QVD files per entity and load date. Qlik's associative model joins on identically named fields, so Datrise standardizes key names so associations link correctly.

Ideal for associative, in-memory exploration in Qlik Sense.

Endpoints

Google Cloud Storage: Object storage for files and data lake exports on GCP.

Qlik: Associative analytics with Qlik Sense apps and governed data models.

How Google Cloud Storage entities map to Qlik

Google Cloud Storage entityQlik objectNotes
bucketsgoogle_cloud_storage_f_bucketsid PK · custom fields → flattened columns for the data model
objectsgoogle_cloud_storage_f_objectsid PK · linked to google_cloud_storage_f_buckets
prefixesgoogle_cloud_storage_f_prefixesid PK · linked to google_cloud_storage_f_buckets
object metadatagoogle_cloud_storage_f_object_metadataid PK · linked to google_cloud_storage_f_buckets

FAQ

How does Datrise handle Google Cloud Storage's custom fields in Qlik?

Flexible values are stored as flattened columns for the data model, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Qlik types.

How does the Google Cloud Storage to Qlik sync stay up to date?

It runs incrementally — Datrise uses incremental QVD loads merged on stable id.

Related pipelines

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